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AWS Panorama Image Classification Example

This is an end to end example that shows how to use an image classification model to classify video frames in the video stream.

Files Included

  • Lambda (Folder)
    • imagenet_classes.py
    • classification.py
    • image-classification.zip
  • Notebook(Folder)
    • Image-Classification-Example.ipynb
    • mt_baker.jpg

mt_baker_output.jpg (Example output) resnet50_v2.tar.gz (Model to Use)

Use Case

  • Classify a video frame using 1000 classes from imagenet using resent50_v2 model.
  • Once a video frame is classified, it can be used as input to perform the business logic.

How to use the Notebook

The included Jupyter Notebook gives a helpful introduction of

  • Task at hand
  • Step by step walk thru of the Panorama SDK / MXNet code
  • Understanding the Lambda structure by creating code in the same format
  • Creating a Lambda function by uploading the included Lambda zip file
  • Publishing the Lambda and displaying the version number and the Lambda console link

Example Output From Notebook

The output displays the top 5 classes the image may belong to.

Example Notebook

How to use the Lambda Function

The included Lambda function is a zip file that can be directly uploaded to the Lambda console to create a usable Lambda arn.

Other resources to use